Secure Medical Data Transmission Research Articles

A blockchain privacy-conserving framework for secure medical data transmission in the internet of medical things

The Internet of Medical Things (IoMT) has transformed healthcare, collecting and transmitting vast medical data. This study proposes an innovative solution, integrating blockchain into IoMT within a fog-cloud computing framework for secure medical data transmission. The blockchain-based zero-trust system ensures reliable data auditing and Electronic Health Repository (EHR) protection. New blockchain entries atop prior blocks deter tampering, with Quad Merkle tree and zero-knowledge proof encryption ensuring data integrity and privacy. The multi-critic deep deterministic policy gradient algorithm optimizes task-offloading decisions in the fog-cloud layer. Security analyses validate its effectiveness, improving computing efficiency and ensuring data fidelity and privacy. These findings position the system as a promising solution for enhancing medical data security and integrity in edge-fog cloud environments, aligning with evolving healthcare technology demands.

Advancements in optical steganography for secure medical data transmission in telehealth systems

Advancements in optical steganography for secure medical data transmission in telehealth systems

Private and Secure Medical Data Transmission Using Wireless Network with QR Code

Abstract: In medical management, more and more information technology is applied to improve work efficiency. For example, the hospital information management system is used for basic information and medical management of patients, and the onedimensional and two-dimensional codes on the wrist are used for quick reading or inputting patient identification (ID), etc. Although computerization is more convenient, due to immature technology or management flaws, there are certain security risks in various typical scenarios, such as disclosure of user privacy through reporting transparency, lack of strict control over the consultation of confidential medical records, the absence of technical verification for the confirmation of the infusion, etc. Identity is easy to fake, payment is inconvenient, etc. Security issues are discussed in more detail below. Healthcare applications are considered a promising area for wireless sensor networks, where patients can be monitored using wireless medical networks (WMN). Current trends in WMN healthcare research focus on reliable patient communication, patient mobility, and energy-efficient delivery.

Private and Secure Medical Data Transmission Using Wireless Network with QR Code

Abstract: In medical management, more and more information technology is applied to improve work efficiency. For example, the hospital information management system is used for basic information and medical management of patients, and the onedimensional and two-dimensional codes on the wrist are used for quick reading or inputting patient identification (ID), etc. Although computerization is more convenient, due to immature technology or management flaws, there are certain security risks in various typical scenarios, such as disclosure of user privacy through reporting transparency, lack of strict control over the consultation of confidential medical records, the absence of technical verification for the confirmation of the infusion, etc. Identity is easy to fake, payment is inconvenient, etc. Security issues are discussed in more detail below. Healthcare applications are considered a promising area for wireless sensor networks, where patients can be monitored using wireless medical networks (WMN). Current trends in WMN healthcare research focus on reliable patient communication, patient mobility, and energy-efficient delivery.

Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model

At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2

Data Ownership and Secure Medical Data Transmission using Optimal Multiple Key-Based Homomorphic Encryption with Hyperledger Blockchain

Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today’s world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.

Blockchain and artificial intelligence enabled privacy‐preserving medical data transmission in Internet of Things

AbstractAdvancements in information technology have benefited the healthcare industry by providing it with distinct methods of managing medical data which improve the quality of medical services. The Internet of Things (IoT) and artificial intelligence are the foundations for innovative sustainable computing technologies in e‐healthcare applications. In the IoT‐enabled sustainable healthcare system, the IoT devices normally record the patient data and transfer it to the cloud for further processing. Security is considered an important issue in the design of IoT networks in the healthcare environment. To resolve this issue, this article presents a novel blockchain and artificial intelligence‐enabled secure medical data transmission (BAISMDT) for IoT networks. The goal of the BAISMDT model is to achieve security and privacy in reliable data transmission of the IoT networks. The proposed model involves a signcryption technique for secure and reliable IoT data transmission. The blockchain‐enabled secure medical data transmission process takes place among the IoT gadgets and service providers. The blockchain technique is applied to generate a viable environment to securely and reliably transmit data among different data providers. Next to the decryption process, the modified discrete particle swarm optimization algorithm with wavelet kernel extreme learning machine model is applied to determine the presence of disease. An extensive set of simulations were carried out on a benchmark medical dataset. The experimental results analysis pointed out the superior performance of the proposed BAISMDT model with the accuracy of 97.54% and 98.13% on the applied Heart Statlog and WBC dataset, respectively.

WITHDRAWN: Secure medical data transmission for DT-WBAN in military environment

One of the latest innovations for the Wireless Sensor Network is the Wireless Body Area Network. WBAN consists of several sensors that can be wearable or inserted into the battalion, such as electrocardiogram, blood pressure, electroencephalography, global positioning system, electromyography, etc. Such sensors continuously track the physical parameter of the battalion by the Local Processing Unit and instantly notify the medical emergency rescue team when the anomalies in the battalion body are identified. Sacrifices of the Battalion and lost their lives due to the absence of motherland medical assistance. In order to safeguard the battalion and also our country, the transmission of medical data plays a major role. In WBAN, providing safe data transmission is the most challenging issue. The Disturbance Resistant Network (DTN) is implemented in the proposed framework to transmit medical data that is explicitly designed for the terrestrial environment [10]. In order to provide efficient and secure communication where traditional protocols fail to use mutual authentication based on group-based registration, DTN protocols are designed to handle harsh conditions. The outcome of the proposed scheme increases protection and reduces the wait to safeguard the battalion and our nation without packet loss.

Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-based Distributed Systems

Memetic Optimization with Cryptographic Encryption for Secure Medical Data Transmission in IoT-based Distributed Systems

A novel chaotic IWT-LSB blind watermarking approach with flexible capacity for secure transmission of authenticated medical images

Nowadays, secure medical data transmission is an essential issue in telemedicine through unsecured channels. Watermarking methods are widely used to provide optimal security for medical image transmission. In this method, a secure and blind watermarking algorithm based on Integer Wavelet Transform (IWT), Least Significant Bit (LSB), and chaotic sequences (ILC) with a high capacity for grayscale medical and non-medical images is ILC. In this scheme, the high-level security is achieved by chaotic sequences that are applied for watermark encryption and determining the location of host blocks and host coefficients in the embedding process. At first, the cover medical image is divided into two distinct parts: the region of interest (ROI) and the region of non-interest (RONI) areas. Due to the high sensitivity of the ROI data in disease diagnosis, no embedding is done in this area. An ROI integrity check data (ICR), produced by some significant features from ROI, to detect tampered blocks inside this area. The main watermark, which is the encryption of ICR along with the patient’s personal and medical information, is embedded in the middle-frequency at the first and second levels of IWT sub-bands. Furthermore, to verify the watermark, integrity check data (ICW) is produced and sent along with other essential data to the receiver. For identifying authentication purposes, the physician’s signature is embedded at the third level (LL3) sub-band of IWT which has the highest energy and robustness. Moreover, the algorithm is flexible for using different sizes of text watermarks and it guarantees a high payload capacity. It is noteworthy the maximum lengths of text watermarks used in the method for 512×512 and 1024×1024 images are 47,784 and 164,620 bits, respectively. Although the size of the embedded watermark increases with the size of the ROI area, the image transparency remains almost constant and also the watermark is extracted with a bit error rate of zero. By doing various analyses, the experimental results represent superior imperceptibility by an average PSNR value of 75 dB. This method provides high imperceptibility and robustness against various attacks compared with state-of-the-art algorithms.

Secure medical data transmission using a fusion of bit mask oriented genetic algorithm, encryption and steganography

This paper presents a bit mask oriented genetic algorithm based secure medical data transmission mechanism. A bit mask oriented genetic algorithm (BMOGA) is utilized to reduce the replication of medical tests data which are transferred across organizations. Medical data is considered very sensitive, therefore secure medical data transmission is must. BMOGA is a variant of the traditional genetic algorithm. Literature reveals that it can avoid premature convergence – a situation when optimization algorithms get stuck at local optimum. BOMGA utilizes Boolean based mask-fill operators and performs reproduction operations in two different phases that helps to avoid premature convergence. Cryptographic features are integrated with the BMOGA for secure data transmission. The encrypted data is embedded into the medical images through 1-level and 2-level Discrete Wavelet Transform (DWT). The reverse process of the BMOGA is implemented for the extraction of secret message from the encrypted one. Numerical experiments are conducted to determine the performance of the proposed algorithm. Results reveals that the proposed algorithm is capable of secure data transmission. Performance comparison is done with the state-of-the-art algorithm with respect to the datasets. Comparative results indicated the superiority of the proposed algorithm in terms of various statistical measures such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM) and mean square error (MSE) to report the results.

A Novel Visual Medical Image Encryption for Secure Transmission of Authenticated Watermarked Medical Images

Secure medical data transmission is the important criteria in tele-medicine. It is helpful for getting the exact medical information at the remote specialist’s side from the local doctor. The medical image watermarking technique is used for secure medical data transmission. Electronic patient record (EPR) is embedded within the original medical image to produce the watermarked medical image for protecting the patient information. The watermarked medical image visually looks like the original medical image. Hence the attacker visually knows the medical image transmission and easily attacks the system. To avoid this situation, image encryption technique is used to change the visual structure of the watermarked medical image. Normal image encryption gives the noise-like or texture-like unknown image format which indicates an existence of some secret information as the encrypted image. To overcome this problem, this paper proposed a visual medical image encryption technique to protect the existence of the watermarked medical image. And to ensure the source authentication, a biometric based authentication is also proposed. The watermarked medical image is encrypted using the proposed visually meaning full image encryption along with the fingerprint image. Experimental results prove that the proposed method yields good results and mitigate the attacker challenges.

Secure medical data transmission by using collaborative neighbour based method in WBAN

Wireless body area network or body area sensor network is wearable device in human body. In online healthcare, transmitting of medical data from sensor node to destination, medical device is used with the member function. However, WBAN has faced many security issues and challenges when transmitting the data. Existing ECC secure key distribution methods are implemented, using this method the time is consumed, efficiency and security of data was discussed. ECC scheme implements for secure key distribution and data exchange. The proposed collaborative neighbour based method is used. Overall, the proposed method proves a better security in healthcare devices. Collaborative neighbour selection and time taken on packet forwarding from one point to another identify the presence of data being appended in the framework. The proposed framework includes enhancement to security and efficiency by forwarding the node information and performing the verification procedure based on the received packet to filter the false data. Extensive experiments and simulation results indicate that, in terms of bandwidth efficiency, packet transmission, network security and processing time, performs favourably compared to existing ECC mechanisms in wireless body area sensor network.

Secure medical data transmission by using collaborative neighbour based method in WBAN

Wireless body area network or body area sensor network is wearable device in human body. In online healthcare, transmitting of medical data from sensor node to destination, medical device is used with the member function. However, WBAN has faced many security issues and challenges when transmitting the data. Existing ECC secure key distribution methods are implemented, using this method the time is consumed, efficiency and security of data was discussed. ECC scheme implements for secure key distribution and data exchange. The proposed collaborative neighbour based method is used. Overall, the proposed method proves a better security in healthcare devices. Collaborative neighbour selection and time taken on packet forwarding from one point to another identify the presence of data being appended in the framework. The proposed framework includes enhancement to security and efficiency by forwarding the node information and performing the verification procedure based on the received packet to filter the false data. Extensive experiments and simulation results indicate that, in terms of bandwidth efficiency, packet transmission, network security and processing time, performs favourably compared to existing ECC mechanisms in wireless body area sensor network.

Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

Quantization based multiple medical information watermarking for secure e-health

In this paper, an improved wavelet based medical image watermarking algorithm is proposed. Initially, the proposed technique decomposes the cover medical image into ROI and NROI regions and embedding three different watermarks into the non-region of interest (NROI) part of the transformed DWT cover image for compact and secure medical data transmission in E-health environment. In addition, the method addressing the problem of channel noise distortion may lead to faulty watermark by applying error correcting codes (ECCs) before embedding them into the cover image. Further, the bit error rates (BER) performance of the proposed method is determined for different kind of attacks including ‘Checkmark’ attacks. Experimental results indicate that the Turbo code performs better than BCH (Bose-Chaudhuri-Hochquenghem) error correction code. Furthermore, the experimental results validate the effectiveness of the proposed framework in terms of BER and embedding capacity compared to other state-of-the-art methods. Therefore, the proposed method finds potential application in prevention of patient identity theft in e-health applications.

Secure Medical Data Transmission by Using ECC with Mutual Authentication in WSNs

Abstract Wireless Body Area Network (WBAN) is a part of Wireless Sensor Network (WSN) which works with medical devices and capable to transmit medical data in run time. It has changed the healthcare industry and proved itself as the best technique in case of emergencies and remote area location. However, WBAN has witnessed several security issues which demand a secure data transmission and user authentication in resource constrained environment. Healthcare devices have resource constraints such as energy constraints, space constraints, etc. Implementing high level security is a daunting task due to those constraints. Proposed scheme implements Elliptic Curve Cryptography (ECC) for secure key distribution and data exchange. ECC provides same level of security by using lesser key size than RSA. Moreover, this scheme has mutual authentication of sink node by base station server and of base station server by sink node. It uses hash value of sink ID, registered random number and timestamp value for two-way authentication. Furthermore, proposed technique uses timestamp value to identify replay attack. Overall, it proves a better security mechanism in case of WSNs for healthcare devices.

Secure Medical Data Transmission in Body Area Sensor Networks Using Dynamic Biometrics and Steganography

In this paper, we have used ECG signal for secure key exchange between two nodes of the same Body Sensor Network (BSN). InterPulsed Interval (IPI) of the ECG signal is considered as a biometric characteristic for key generation because of its high randomness, time variance properties and also to avoid interference between the other BSNs. The body sensor node to base station (PDA or Laptop) communication has been secured using the symmetric cryptographic algorithm AES and integrity is checked by using the Message Authentication Code (MAC). Steganography is used for securing the data transfer between base station and medical server. In this work, we have embedded the medical data (Blood Pressure) within the patients' facial image. The face image is selected as a cover image in order to provide authentication. The pixel positions for embedding the medical data are determined by fixing a threshold level (120-126). This threshold value is chosen so as to have high random distribution throughout the image. The results have been shown using MATLAB 7.8.0.

A new telemonitoring system intended for chronic heart failure patients using mobile telephone technology — Feasibility study

Background Remote monitoring is one modality of structured care in chronic heart failure. The purpose of this study was to evaluate the feasibility of a new wireless telemonitoring system via a mobile phone network. Methods Portable home devices for electrocardiogram, blood pressure, body weight and self-assessment measurements were connected (via Bluetooth®) to a personal digital assistant (PDA) that performs automated encrypted transmission via mobile phone. Two telemedical centres were set-up. Results 30 healthy volunteers were enrolled and followed for 26 days. A total of 4002 single measurements were taken, 133 ± 37 per person. No data was lost or incorrectly allocated. 880 of 937 (94%) of the ECG recordings had sufficient diagnostic quality for rhythm analysis and single beat measurements. 50 continuous ECG-streams (312 min) without disruption were performed. Total system availability was 96.6%, including that of the mobile phone network. Conclusions Mobile phone technology is suitable for continuous and secure medical data transmission. To evaluate the clinical use in chronic heart failure patients, a large multicentre randomized controlled trial (ClinicalTrials.gov Identifier: NCT00543881) was started.

Novel Efficient and Secure Medical Data Transmission on WiMAX

This paper discusses an improved technique for bandwidth-efficient and secure transmission of medical records over simulated orthogonal frequency division multiplexing (OFDM)-based Worldwide Interoperability for Microwave Access (WiMAX)/IEEE 802.16 physical layer. Medical data compaction along with security has been achieved by embedding electrocardiogram (ECG) data into Haar wavelet coefficients of an x-ray image that is transmitted over the channel. The compaction ratio and peak signal-to-noise ratio (PSNR) between the transmitted and received x-ray images for visual clinical performance have been evaluated. It is shown that 12.5% data compaction along with true reception of ECG signal can be achieved for channel signal-to-noise ratio (SNR) >or= 9.6 dB while the achieved PSNR was 55.97 dB. The PSNR value is well above the minimum acceptable 35 dB level, making it potentially useful for secure and bandwidth-efficient transmission of clinical records.